US project explores sensors, AI and drones for structure repairs

sensors AI drones
The project is examining ways to use modern techniques for structure repairs

Artificial intelligence, drones, sensors, and fibre-reinforced polymers (FRP) are being combined in a $7.7m (£6.2m) project for dam and levee repair.

The five-year project in the US will look at ways to examine these structures to avoid inspections by rope, improve monitoring, and extend their lifespan through timely repair and the use of new materials.

Researchers at the University of Kansas (KU) School of Engineering are part of the project. Associate professor Jian Li, one of the project leads, said: “My main role is focused on using deep learning and computer vision to autonomously identify the location and severity of dam damage, such as concrete cracking and spalling, for which FRP repair is needed.

Self-sensing repairs

“Once the repair is done, these locations are no longer inspectable. Therefore we’ll also develop self-sensing FRP repairs to enable continued monitoring of the repaired regions to ensure long-term safety.

“By leveraging emerging technologies including artificial intelligence, computer vision and advanced sensing, our research will greatly enhance timely repair, retrofit and maintenance of the nation’s large inventory of concrete dams.” 

The KU team will research FRPs for repair of concrete gravity dams and will test the material against huge loads in flexible and direct-shear tests.

Avoiding disaster

The project is a collaboration between KU, the US Army Engineer Research and Development Center, the department of Homeland Security’s Science and Technology Directorate and the US Army Corps of Engineers.

Many US dams and levees built in the 1930s are worse for wear and some estimate repair costs at $93.6bn (£75.6bn).

In 2005 New Orleans’ levees breached, causing disaster during Hurricane Katrina. Levees in South Carolina were breached during Hurricane Matthew in 2016. The following year heavy rain caused the Oroville Dam in California to fail.

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